Combining team driven KM with technology

Background
McLaughlin (2007) reflects on Nonaka’s model and has identified that resource limitations in some organizations will require the need to be particular about the types of projects that Knowledge management (KM) is directed to. He also states that the mode of transfer would be either:

• Codified: technology driven to mange explicit knowledge
• Personalised: team driven to manage tacit via a team dynamic

After reading this I wondered if there was actually a hidden third option that was missing: personalised systems that are driven by technology. This may already be covered in other literature but it did not come across explicitly in the texts that I read.

An organization with limited resources could encourage small communities of practice (COP) that discuss specific issues: essentially tacit to tacit to encourage team working and exchange and creation of new knowledge. However many small organisations now include a virtual element: people working from home or with satellite branches in other countries. If these organizations used video conferencing combined with an audio transcription software package then the tacit knowledge could be more readily codified and the tacit knowledge could then be available to be replayed by any COP or member of the organization. There is an interesting and short video on this which you can view below.



Examples in practice
In the case of Albion Fabrics (AF) there is a pressing issue. Various teams within the organisation each have a responsibility for a particular client and the garments that are supplied to them. Many of the teams may actually use similar factories to construct the garments and there have been increasing problems with the quality and delivery times of the garments from those factories. A COP could be formed with representatives of the teams with a general mandate to improve customer service focusing on relationship with the factories and reducing potentially costly errors. These meetings could be videoed and the audio transcribed allowing people to discuss ideas freely without worrying about note taking during the even. The COP could even be extended, when appropriate, to include representatives of factories via video conferencing so that problems could be discussed and transcriptions shared between the members. Nonaka(2007) discusses the success of Japanese firms in the field of knowledge management and suggests that a powerful tool in transferring tacit to explicit knowledge lies in the store of “figurative language and symbolism”. This idea can later be incorporated by using a cognotivistic approach of mind mapping to help visualise ideas, concepts and points within the COP although additional training would be required. This is a common practice in education and Barth(2006) refers to software mind map versions being used in meetings by KM practitioners and there are packages now that can create mind maps direct from speech such as MindManager 8. Tony Buzan has done extensive research into mind mapping as an educational tool with further information below.


Conclusion
Although this method would initially require trust from the COP to enable them to feel relaxed in the presence of a video camera, or use of video conferencing, there is a great potential. Combining the face to face meeting with video conferencing would require a minimum cost outlay and could quickly harvest knowledge within a COP. With additional training of key staff in mind mapping techniques, there is plenty of scope to enable codification of ideas and support a range of what McLaughlin (2007) describes as “different knowledge profiles” required by an organization.


BARTH, S., 2006. Mapping the mind's eye. KM World, 15(6), pp. 14-14.

NONAKA, I., 2007. The Knowledge-Creating Company. Harvard business review, 85(7), pp. 162-171.

MCLAUGHLIN, S., 2007. Managing knowledge for success. Engineering Management, 17(5), pp. 42-45.

Can an organization also be viewed as a community of practice?

There are several well defined viewpoints of what constitutes an organization and what constitutes a community of practice (COP). An organization can be a structure that processes a resource from its environment to produce an output Laudon and Laudon (2006) where as a COP according to Wenger (Unknown) is “groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly”.

There are some organizations whose mission is to promote knowledge and awareness within a particular area. One such example is the Good Homes Alliance (GHA). Within this organisation there are several correlations to those found in a community of practice. Using the GHA as an example we can compare the characteristics outlined by Wenger for a COP with those of the GHA.

Characteristic	COP: Wenger (unknown)	GHA (http://www.goodhomes.org.uk)
Domain	A shared domain of interest.	Sustainability
	A Commitment	Committed to improve sustainability homes throughout UK
	A shared competence	Expert contribution from both member and non member bodies
Community	Engage in joint activities and discussions.	GHA provides government advice on sustainability. Exhibitions Discussion forum
	learn from each other	Learning events and conferences Open source research materials Membership only research materials.
Practice	Practitioners developing a shared repertoire of resources	Member and non member Practitioners: Green Oak Housing Bioregional Quintain Swan Country Homes UCL Leeds Metropolitan University Aiyana Ltd

In direct comparison between GHA and Wenger’s characteristics the similarities are striking however Seaman(2008) presents two points that could be taken into account.

Firstly that a COP develops informally and secondly that a COP follows specific stages of development: “potential, coalescing, active, dispersed and memorable”. An organization such as the GHA does not develop informally. The life cycle suggest by Seaman(2008) implies the COP has a finite usefulness although the same could be said about any organisation, its services or products. In the case of the GHA, if all houses were built with sustainability in mind they may then need to re-evaluate both their mission statement and business model. Interestingly a similar model is outlined by Slack et al(2007) to identify a products or service’s life cycle although the memorable stage is not presented: introduction, growth, maturity and decline.

Seaman(2008) does note that “all members share a common goal and all contribute to meet that goal” which is echoed in the GHA mission statement:

“group of housing developers, building professionals and sustainability experts, whose aim is to build and promote sustainable homes and communities, and to transform the whole of mainstream UK house building into a sustainable endeavour”

Conclusion

There is no suggestion in any of the literature that I have found that an organisation can be a COP, yet if we look at both definitions presented here they could be considered interchangeable: A COP produces an output (knowledge) from the resources of its environment; an organisation can be a group of people that share a common passion (GHA).After contacting a member of the GHA I was interested to find that the organisation actually formed from a group of practising professionals all concerned with, and interested in, sustainability meeting regularly to discuss issues and deciding to create a more formalised alliance to tackle the problem.

In conclusion I feel that organizations such as the GHA do share both a cultural and operational similarity to the definition and ethos of a COP and as such may well be the exception to the rule.

References

Good Homes Alliance (2005),Mission statement, available at: http://www.goodhomes.org.uk/mission/, [Accessed 3 February 2009]

Laudon, J., Laudon, K. (2007). Management Information Systems- Managing the digital firm, 10th edition. Harlow, Essex: Pearson Education

Seaman, M., 2008. Birds of a Feather? Communities of Practice and Knowledge Communities. Curriculum & Teaching Dialogue, 10(1; 1), pp. 269-279.

Wenger ,E., (unknown) Communities of practice : a brief introduction: [online] available at : http://www.ewenger.com/theory/ [accessed 11 February 2009]

Epistemological Knowledge Management

Vast is not a big enough word
Before I began the investigation into this area I had little preparation for the sheer depth , breadth and ancestry that this word concealed.
The term Epistemology can encompass vast areas of philosophical study that has generated heated debate across hundreds of years. Hirschheim(Unknown) cites Snyder's observations on eastern cultures' development of epistemology as far back as 600 BC. With that much history behind a word there would inevitably be a lot of reading ahead. In order to try and focus my research I attempted to ascertain simple definitions to aid my focus.

Definitions

The Stanford Encyclopaedia of Philosophy gives a broad definition: "study of knowledge and justified belief". There seem to be several viewpoints from this broader definition as further investigation reveals several divisions of epistemology. Gebert et al (2002) view epistemology as an investigation of knowledge itself but continue to sub categorize this into:
1/ Cognitivistic: rule based which organises knowledge into distinct structures which may exist independently to the individual.

2/ Autopoietic: context sensitive knowledge and embodied in the unit, which we can represent as an individual or an organisation. This word means self creation as is commonly used to refer to a biological entity or living system which has a boundary and may contain components (sounds like an organization to me ! )
To give examples of these I found it easier to relate these to my teaching experience.

Cognitivistic: In programming classes, teaching someone the rules associated with the difference between a Function and a Subroutine within visual basic. Then asking them to create examples of each type of coding practice. The student will then go through the process of learning by doing which will help them create their own sense of what the differences are. The student then builds upon those knowledge structures as they continue to study other programming languages cross referencing and transferring that knowledge to other programming languages and adapting what they have learnt.



Autopoietic:My initial view from the definition is if knowledge is context sensitive it is biased. Again using the teaching perspective I can look at autopoietic from both an individual or an organizational viewpoint. If I teach a set of students the concepts of structuring a program in visual basic, my experience and knowledge context provides a bias to how I then present my knowledge.
We can see direct examples of this in the KM workshops as each lecturer presents a concept and shares their knowledge of that concept biased by their own experiences. I could also consider that in the past my organisation may have had a bias to the way in which programming courses should be taught influencing the emphasis of certain areas of knowledge over others. Das(1999) makes reference to individual bias this and cites Krogh et al and comments on this in relation to management decision making whereby knowledge created by highly dynamic managers is potentially biased.

Jackson(2007) completed a more thorough investigated into framing this to an organisation and is unsure that autopoietic knowledge viewpoint can be successfully applied. One reason being that the components of the system, the employees, are also by definition autopoietic.

From the sublime to the ridiculous and back again
My initial investigations have lead me from excessively broad to incredibly focused and although this may not have been exactly the task it has broadened my understanding of epistemology and its relation to knowledge management.

If I reflect on my own experiences, I follow a very practical learning by doing approach:cognitive school of thought. Within a group environment or an organisation this will differ between person to person. If an organisation can be viewed as studying knowledge and accumulating it, autopoietic, then a successful KM approach would have to balance between the knowledge needs of the organisation as an entity as well as those within it.



Adendum: Knowledge management practice from an autopoietic and cognitive epistemological perspective.

Looking at this type of epistemology from the context of teaching, I was asked how this could offer practical help to an organisation. If we consider a teaching environment such as the example I gave then a knowledge management strategy could consist of a hybrid of both cognitive and autopoietic frameworks as a starting point. Gebert et al (2002) focused on the autopoiesis relating to Customer Relationship Management(CRM) but the same framework could be adapted to education.

A teaching organization could be described as a learning entity where the core competency is dissemination, research, creation, deconstruction and when appropriate replacement or removal of knowledge Information and data. Hall(2005) attributes similar activities to an organisation as a learning entity and suggests that these are "serving to maintain organisational integrity in a dynamic economic environment". One example of a supporting KM tool is a virtual learning environment (VLE). Such a tool can structure a knowledge management strategy from both a technological perspective and provide a platform for discussion forums, internal blogs and e-mail facilities allowing for social and cultural diversity.

The VLE, if managed properly, can represent a substantial knowledge repository for both lecturer and student alike. Taken to the extreme the VLE can represent the boundary of identity for the organisation itself and which is exemplified in the model used by the Open University dealing predominantly with distance learners: "Identity can be a source of competitive strength by distinguishing the organization from potential competitors", Santos and Eisenhardt (2005) .

The transient flow of both staff and pupils to and from the organization is immaterial, what is important is the institution as an entity promotes growth, self maintenance and preservation of its body of knowledge in a symbiotic relationship with those staff and students that the system comprises of at any one time: the system components. The Identity boundary of the organization will expand as the knowledge grows and will attract a greater depth and breadth of transient components( students and staff) all benefiting from and contributing to the treasury of knowledge.

I agree with both Jackson(2007) and Hall(2005) who both state that further research into this area needs to be done to clarify this viewpoint. As a conceptual frameworkt, viewing the organisation as a learning entity seems a very reasonable starting point with which to build a KM strategy and with most organisations, regardless of the industry, choosing an appropriate and flexible framework from which to build seems one of the greatest hurdles.




References

DAS, T. and Bing-Sheng, (1999). Cognitive Biases and Strategic Decision Processes: an Integrative Perspective. Journal of Management Studies, 36(6), pp. 757-778.

Gebert, H., Geib, M., Kolbe, L.M., Riempp, G. (2002),Towards customer knowledge management:integrating customer relationship management and knowledge management concepts, Second International Conference on Electronic Business 2002 Conference Proceedings, Taiwan.,pp (unknown)

Hall,W.,(2005),Biological nature of knowledge in the learning organisation,The Learning Organization,12(2), pp.169 - 188

Jackson, T,W. 2007. Applying autopoiesis to knowledge management in organisations, Journal of Knowledge Management,11(3),pp.78-91

Santos, F.M. and Eisenhardt, K.M., (2005). Organizational Boundaries and Theories of Organization. Organization Science, 16(5), pp. 491-508.

Stanford Encyclopedia of Philosophy, (2005),Epistemology, available at http://plato.stanford.edu/entries/epistemology/, [Accessed 2 February 2009]

Wikipedia (unknown), Autopoiesis, available at http://en.wikipedia.org/wiki/Autopoiesis, [Accessed 2 February 2009]

Relationships between data, information and knowledge

Initial assumptions

Like many people from an Information Systems background I have followed the path of texts where the concepts of data and information are introduced. Bocij et al,(2006) provides examples of data such as raw facts, numbers or non-random symbols. Information is derived from a process or interpretation of data which then provides the data with meaning for the observer. Knowledge is more difficult to pin down. It is described by Turban et al,(2006) as " Information that contextual, relevant and actionable". Chaffey(2006) suggests knowledge is: " the next level of sophistication or business value in the cycle from data through to information to knowledge."

I was surprised to find similar representations in more knowledge management centred papers . Kakabadse et al (2003) suggest "the chain of knowledge flow is data-information-realization-action/reflection-wisdom" yet still represent this as a linear diagram. This approach to the representation of the the relationship between the entities: data, information and knowledge is commonly presented to the reader in a format similar to the one below which appears to stress a sequential and unidirectional flow from one state to the next.



Previously I had accepted these definitions and could relate them to my programming background. An example could be such that a either singularly or collectively figures could be considered data. Processing that data along with other relevant data together in using a database could produce a record which can then be considered information. Further records on the customer can be analysed to produce knowledge about the customers spending habits and then produce a flag if abnormal spending habits occurred. Example of this in action when your debit card is declined when purchasing an item outside of your normal spending habit and a security call is required by you to confirm that you are the one making the purchase.



Re-examination of the assumptions

The standard assumption that has been presented so far is that knowledge is derived from information which in turn is processed data. Upon further study the term "knowledge" now seems less defined and unlikely to be the product of a translation from data to information to knowledge, constricted by a distinct linear progression. Stenmark(2002) identifies this a problem when he states that many texts provide make an over simplification of the relationship between data, information and knowledge.

"it may not be possible to correctly state the true relationship between these entities, there is nothing that indicates that is should be linear"

Tuomi (2000) makes two comments on the linear viewpoint, the first being:

"raw data do not exist, and that even the most elementary perception is already influenced by potential uses, expectations, context, and theoretical constructs"

This implies that even though standard practice is to assume data as the starting point from which to build knowledge, the data itself must have already been interpreted in some way to actually exist; it did not just magically materialise. A second point from Tuomi (2000) states:

"data emerge only after we have information, and that information emerges only after we already have knowledge"

Tuomi has presented this idea within his "reversed hierarchy" model and is taken in the context of the construction of a database and Stenmarks findings also relate to an information systems context. Although this may have some bearing, the concepts could be transferable to other situations or contexts.. If we take both the common idea of presenting the progress of data through to knowledge as a linear representation and combine that with Tuomi's reverse hierarchy then we could argue the idea of knowledge positioned within a cycle of events where each entity within the cycle is dependant on the other.


I have tried to express a simplification of that in the following diagram:( you may need to click on it to expand the diagram fully)



The diagram identifies areas of study with knowledge at the intersection.
On the left hand side I have included the original idea of data, information and knowledge. This can be expanded to illustrate that from knowledge it is possible to be redirected back to more specific information or the need for more specific data which in turn reinforces that knowledge. This is an attempt to incorporate the "reversed hierarchy" model proposed by Tuomi(2000). The right hand side also illustrates that knowledge may either create or give structure to new data, or information, or that data or information from another area of study can reflect upon and thus increase the breadth and depth of knowledge. This may then be fed back into the left hand side loop. At all times the relationship between data information and knowledge is cyclical rather than linear.

Example in practice

Neef et al (1998) suggest there are four different types of knowledge:know-what; know -why; know-how and know -who. What I found interesting was the crossover between what can be identified as information and knowledge within their explanations.

Relating this to a particular context this could be illustrated by managing the construction of a web development project within an web consultancy practice:

what should the website be constructed from? e.g. Colour, number of pages, fonts used. Complex areas needing knowledge to assemble and anlayze information translated from facts.

why should the website be arranged in this way? e.g. Knowledge of marketing principles and human behaviour that identify the way a person uses a website and why you should present it in a certain way.

how should the project be structured? e.g. Knowledge of time scales, whether or not to create a prototype, managing the customer to ensure that they are informed of progress or potential problems.

who should be involved and to what extent? e.g. Selecting a project based team to complete a project which may involve identifying lack of skills in a particular area so that contractors may be introduced for the project duration.

Consider the viewpoint of a consultant managing the project. The knowledge that a consultant has will need to be presented and communicated to in order to successfully complete the project. This process will include analysing ideas and knowledge concepts that can then be expressed and exchanged in a format such as documents: information. Glushko and McGrath(2008) identify this process as document engineering. The information may need to be stored and consequently split into data Tuomi(2000) . The data itself may then be used to create new information that can create further knowledge and creative ideas to complete the project successfully or infuse it with a more inspirational approach previously unexplored.



Conclusion

Although these ideas have been expressed from the bias of computing context, there seems a strong case for the relationship between data, information and knowledge not to be seen not as a a one way relationship but rather as a cycle: observation, analysis, sense making and deconstruction. Each iteration providing deeper insight and avenues for exploration and application within existing or new areas of study.


References

Bocij, P., Chaffey, D., Greasley, A., and Hickie, S. (2006). Business Information Systems, 3rd edition. Harlow, Essex: Pearson Education

Chaffey,D. (2006). E-Business and E-Commerce Management 3^rd edition , Harlow, Essex: Pearson Education.

Glushko,R., McGrath,T,. (2008) Document Engineering:Analyzing and Designing Documents for Business Informatics and Web Services, Massachusetts,USA: MIT Press

Kakabadse, N., Kakabadse, A. and Kouzmin, A. (2003), “Reviewing the knowledge management literature: towards a taxonomy”, Journal of Knowledge Management, vol.7, no.4, pp. 75-91.

Laudon, J., Laudon, K. (2007). Management Information Systems- Managing the digital firm, 10th edition. Harlow, Essex: Pearson Education

Neef,D.,Siesfeld,G.,Cefola,J.(1998), The Economic Impact of Knowledge,Butterworth-Heinemann,available at http://books.google.co.uk/books?id=ItnyIjP6uUYC&dq=knowledge+definition&lr=&source=gbs_summary_s&cad=0, [Accessed 2nd February 2009]

Stenmark, D. (2002). "Information vs. Knowledge: The Role of intranets in Knowledge Management". In Proceedings of HICSS-35, IEEE Press, Hawaii, January 7-10, 2002, available at http://www.viktoria.se/~dixi/km/chap3.htm, [Accessed 10th February 2009]

Tuomi, I., (2000). Data Is More Than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory, Journal of Management Information Systems, 16(3), pp. 103-117.

Turban, E., Leidner, D.,McLean, E.,Wetherbe,J.(2006) Information Technology for Management, 5th Edition, Massachusetts: Wiley